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Question: How to prove this theorem? Let \[A\] be an \[n \times n\] matrix and \[\lambda \] an Eigenvalue of \...

How to prove this theorem? Let AA be an n×nn \times n matrix and λ\lambda an Eigenvalue of AA. Then λ+μ\lambda + \mu is an Eigenvalue of the matrix M=A+μIM = A + \mu I where II is the n×nn \times n unit matrix.

Explanation

Solution

Here, we will use the definition of the Eigenvalue for a matrix. Then we will use the given matrix and multiply an Eigenvector. Then we will substitute the Eigen value to simplify the equation. We will use the condition of linearity and Identity to prove the statement.

Formula Used:
We will use the following Properties:

  1. Linearity Property: f(ax+by)=af(x)+bf(y)f\left( {ax + by} \right) = af\left( x \right) + bf\left( y \right)
  2. Identity Property: AI=A=IAAI = A = IA

Complete step by step solution:
We are given that λ\lambda is an Eigenvalue of then×nn \times nmatrixAA.
We know that by definition, if Aν=λνA\nu = \lambda \nu for ν0\nu \ne 0, we say that λ\lambda is the Eigenvalue for ν\nu and ν\nu is an Eigenvector for λ\lambda .
Aν=λνA\nu = \lambda \nu ……………………………………………………(1)\left( 1 \right)
Now, we will prove that λ+μ\lambda + \mu is an Eigenvalue of the given matrix M=A+μIM = A + \mu I where II is the n×nn \times n unit matrix.
Now, let us consider the given matrix
M=A+μIM = A + \mu I
By multiplying the Eigenvector ν\nu on both the sides of the given matrix, we get
Mν=(A+μI)ν\Rightarrow M\nu = \left( {A + \mu I} \right)\nu
Using the Linearity Property f(ax+by)=af(x)+bf(y)f\left( {ax + by} \right) = af\left( x \right) + bf\left( y \right), we get
Mν=Aν+μIν\Rightarrow M\nu = A\nu + \mu I\nu
Now, it is given that II is the n×nn \times n unit matrix, so by using the identity property AI=A=IAAI = A = IA, we get
Mν=Aν+μν\Rightarrow M\nu = A\nu + \mu \nu
Now, by using the equation (1)\left( 1 \right), we get
Mν=λν+μν\Rightarrow M\nu = \lambda \nu + \mu \nu
Again by using the Linearity Property f(ax+by)=af(x)+bf(y)f\left( {ax + by} \right) = af\left( x \right) + bf\left( y \right), we get
Mν=(λ+μ)ν\Rightarrow M\nu = \left( {\lambda + \mu } \right)\nu
So, λ+μ\lambda + \mu is an Eigenvalue of the matrix MM and ν\nu is the corresponding Eigenvector of the matrix MM for the Eigenvalue λ+μ\lambda + \mu .

Theorem: Let AA be an n×nn \times n matrix and λ\lambda an Eigenvalue of AA. Then λ+μ\lambda + \mu is an Eigenvalue of the matrix M=A+μIM = A + \mu I where II is the n×nn \times n unit matrix.
Hence proved.

Note:
We know that an Eigenvector of a matrix is a non-zero vector in Rn{{\bf{R}}^n} such that Aν=λνA\nu = \lambda \nu for some scalarλ\lambda . An Eigenvalue of a matrix is a scalar λ\lambda such that Aν=λνA\nu = \lambda \nu has a non-trivial solution. We should note that an Eigenvector and an Eigenvalue are found only for square matrices. Also, an Eigenvector is always non-zero but Eigenvalues may be equal to zero. A square matrix is defined as a matrix where the number of rows is equal to the number of columns.